Getting and Cleaning Data Course Project

Description

Dataset name: Human Activity Recognition Using Smartphones Data Set

The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data.

The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain.

Check the README.txt file for further details about this dataset.

A video of the experiment including an example of the 6 recorded activities with one of the participants can be seen in the following link: [Web Link]

An updated version of this dataset can be found at [Web Link]. It includes labels of postural transitions between activities and also the full raw inertial signals instead of the ones pre-processed into windows.

• Citation: Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.

• URL: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

• Date published: 2020-07-27

• Creator:Sandeep Kiran Gudla

  • keywords: Subject_Name, Activity_Name, tBodyAcc-mean()-X, tBodyAcc-mean()-Y, tBodyAcc-mean()-Z, tBodyAcc-std()-X, tBodyAcc-std()-Y, tBodyAcc-std()-Z, tGravityAcc-mean()-X, tGravityAcc-mean()-Y, tGravityAcc-mean()-Z, tGravityAcc-std()-X, tGravityAcc-std()-Y, tGravityAcc-std()-Z, tBodyAccJerk-mean()-X, tBodyAccJerk-mean()-Y, tBodyAccJerk-mean()-Z, tBodyAccJerk-std()-X, tBodyAccJerk-std()-Y, tBodyAccJerk-std()-Z, tBodyGyro-mean()-X, tBodyGyro-mean()-Y, tBodyGyro-mean()-Z, tBodyGyro-std()-X, tBodyGyro-std()-Y, tBodyGyro-std()-Z, tBodyGyroJerk-mean()-X, tBodyGyroJerk-mean()-Y, tBodyGyroJerk-mean()-Z, tBodyGyroJerk-std()-X, tBodyGyroJerk-std()-Y, tBodyGyroJerk-std()-Z, tBodyAccMag-mean(), tBodyAccMag-std(), tGravityAccMag-mean(), tGravityAccMag-std(), tBodyAccJerkMag-mean(), tBodyAccJerkMag-std(), tBodyGyroMag-mean(), tBodyGyroMag-std(), tBodyGyroJerkMag-mean(), tBodyGyroJerkMag-std(), fBodyAcc-mean()-X, fBodyAcc-mean()-Y, fBodyAcc-mean()-Z, fBodyAcc-std()-X, fBodyAcc-std()-Y, fBodyAcc-std()-Z, fBodyAcc-meanFreq()-X, fBodyAcc-meanFreq()-Y, fBodyAcc-meanFreq()-Z, fBodyAccJerk-mean()-X, fBodyAccJerk-mean()-Y, fBodyAccJerk-mean()-Z, fBodyAccJerk-std()-X, fBodyAccJerk-std()-Y, fBodyAccJerk-std()-Z, fBodyAccJerk-meanFreq()-X, fBodyAccJerk-meanFreq()-Y, fBodyAccJerk-meanFreq()-Z, fBodyGyro-mean()-X, fBodyGyro-mean()-Y, fBodyGyro-mean()-Z, fBodyGyro-std()-X, fBodyGyro-std()-Y, fBodyGyro-std()-Z, fBodyGyro-meanFreq()-X, fBodyGyro-meanFreq()-Y, fBodyGyro-meanFreq()-Z, fBodyAccMag-mean(), fBodyAccMag-std(), fBodyAccMag-meanFreq(), fBodyBodyAccJerkMag-mean(), fBodyBodyAccJerkMag-std(), fBodyBodyAccJerkMag-meanFreq(), fBodyBodyGyroMag-mean(), fBodyBodyGyroMag-std(), fBodyBodyGyroMag-meanFreq(), fBodyBodyGyroJerkMag-mean(), fBodyBodyGyroJerkMag-std() and fBodyBodyGyroJerkMag-meanFreq()